why was this analysis done? - world bank · barron, batanayi muzah university of new south wales/...
TRANSCRIPT
The South African Government has committed to the globally promoted
90-90-90 scale-up targets and shares the vision of Ending AIDS by 2030
(Sustainable Development Goal time horizon, 95-95-95 targets). In 2012, it was
estimated that in South Africa, two-thirds of new HIV infections occur in urban
areas (over 300,000), and that the incidence rate in informal urban settlements was at
2.5% compared to 1.1% nationally.1
WHY WAS THIS ANALYSIS DONE?
The Gauteng cities of Johannesburg, Ekurhuleni and Tshwane, and the KwaZulu-Natal metro of
eThekwini, are the health districts (metros) driving the national HIV statistics. The Johannesburg
Health District alone is thought to have 22% of all people living with HIV (PLHIV) in any of the
eight metros.2,3
Johannesburg District has a population of approximately 4.8 million and annual
population growth of about 3%.4
Cities and metropolitan areas offer both scope and opportunities to take the country closer to
the 90-90-90 targets due to the concentration of population and HIV burden. South Africa’s eight
metros only cover 2% of the national territory but account for 39% of the country’s population.
They are responsible for 70% of the Gross Domestic Product, and contain half of all
unemployed South Africans.5 The metros are vital areas for HIV and health interventions to
succeed due to their economic importance for national prosperity.
WHY IS ALLOCATIVE EFFICIENCY IMPORTANT?
Given the size of the South African HIV epidemic and the associated health care costs,
allocating HIV resources optimally at local level remains a national priority. The health district is
the unit for HIV planning and resource allocation and all 52 districts have a District
Implementation Plan (DIP) for programme scale-up from FY2016/17. The South African DIP
process in 2015 showed that relatively little analytical/modelling evidence is available to support
target-setting and district-level decision-making on resource allocation. While there is effective
antiretroviral treatment (ART) available, there are other proven interventions such as medical
male circumcision, condoms, and pre-exposure prophylaxis (PrEP) for sex workers, which
showed up as key response elements in the National HIV Investment Case.6 There are also
novel service combinations such as the DREAMS package for young women and adolescent
girls. With these various interventions available, the country intends to reach the internationally
promoted 90 targets. The necessary scale-up of HIV services needs to take into account the
epidemic and demographic dynamics of the city, and allocate HIV resources in an optimal way
for impact.
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ENDING AIDS IN JOHANNESBURG OCTOBER 2016
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HOW WAS IT APPROACHED?
The analysis was conducted in partnership with the South African government, an academic
institution (Burnet Institute) and the World Bank. Optima HIV was used for allocative efficiency
analysis. This software provides epidemic modelling, resource projections for reaching specific
targets, and impact/cost-effectiveness analysis. The special and unique features of Optima HIV
are: high flexibility/customisable structure, its mathematical optimisation function for allocation of
resource envelopes, and ability to include constraints to reflect real-life policy scenarios.7
Optima HIV has helped decision-makers, programme managers, and funding partners plan for
maximum impact with the funding available for the HIV response and hence work toward
improved efficiency and sustainability.8
The analysis built on South Africa’s 2015 HIV investment case and linked to the DIP targets. It
took into account the general population based and sex work-based transmission networks.
Data on the HIV epidemic and response, HIV expenditure, population size estimations, among
others, were captured in Optima HIV,9 the available DREAMS plans for Johannesburg Health
District were reviewed, and cost functions established. The epidemic model was calibrated
using data from 2000–15 on 26 population groups to ensure best fit. Stakeholder discussions
were held in a data workshop and the Durban fast-track meeting in March 2016.
KEY JOHANNESBURG FACTS AND DATA
In 2016, an estimated 12% of the population are HIV infected and there are close to
600,000 PLHIV (Optima HIV model estimates)
There are approximately 25,000 new HIV infections and approximately 8,500 HIV-
related deaths in 2016 (Optima HIV model estimates)
The 4.8 million strong population includes 500,000 children below 5 years of age, 647,000
children aged 5–14, 441,000 females aged 15–24, and 372,000 males 15–24 (extrapolated
from 2011 population census)
It is estimated that the female sex worker population is approximately 15,000, the
MSM population about 94,000 and that there are about 1,400 persons who inject
drugs (with high mobility to e.g., Tshwane District) (estimates from key population surveys)
The human development index is 0.72 (0.64 nationally, 2014 data: HIS Global Insight)
About 15.6% of the population lives below the food poverty line (23.2% nationally,
2014 data: HIS Global Insight)
Functional literacy is at 92% (82% nationally, 2014 data)
In 2014/15, about Rand 900 million public sector funding was allocated to
Johannesburg’s HIV response (Johannesburg District expenditure data 2014/15, Gauteng DOH)
Over 80% of these conditional grant and voted funds for the HIV response were allocated
to HIV treatment (Johannesburg District expenditure data 2014/15, Gauteng DOH)
ENDING AIDS IN JOHANNESBURG OCTOBER 2016
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Model-derived HIV care cascade, Johannesburg (2010–15)
Epidemic projections for baseline vs. HCT/ART scale-up, Johannesburg (2010–30)
WHAT WERE THE MAIN FINDINGS?
1. Johannesburg has rapidly expanded HIV diagnosis and treatment between 2010 and 2015, reaching 267,236 PLHIV with the ART programme in 2015
Due to population
growth in the city
and the life-
extending effects
of ART, the
number of PLHIV
steadily grew.
More and more
PLHIV know about
their positive
status (an
estimated 70% by
2015). While about
64% of diagnosed
PLHIV access
treatment, only
about 54% of them
were known to be virally suppressed in 2015.
2. The projected health impact of successfully scaling-up HIV testing (HCT), treatment and ART adherence to the 2020 and 2030 SDG target levels is very large in Johannesburg
The increase in PLHIV on
treatment results in reductions
in new HIV infections, a
cumulative difference of ~327
thousand infections from
2016–30. It also results in
reductions in HIV-related
deaths, a cumulative
difference of ~104 thousand
deaths from 2016–30.
The reduction in new
infections combined with the
reduction in deaths results in a
net decrease in the number of
PLHIV, leading to a decline in
HIV prevalence.
Note: Colours of lines red =
maintain coverage; orange =
90-90-90 by 2020 and
95-95-95 by 2030
ENDING AIDS IN JOHANNESBURG OCTOBER 2016
4
Modelled HIV care cascade to 2020, Johannesburg (if reaching 90 targets)
3. The dynamic Optima HIV model provides target numbers for planning and factors in the prevention effect of ART on the future PLHIV numbers
Under the HCT/ART
scale-up scenario,
there are 31
thousand fewer
PLHIV in 2020
(left-hand brown
bar) compared to
the baseline of
constant ART
coverage at 2015
levels. A smaller
pool of PLHIV
means the 2020
and 2030 targets
become more
feasible. An
additional ~162
thousand HIV
diagnoses would be required to reach the first 90 by 2020.
An additional ~257 thousand PLHIV would need to be on ART to attain the second 90 (=81% of
PLHIV on ART by 2020). This means almost doubling the 2015 ART patient number. However,
by 2020 most of the scale-up and expansion work would have been achieved, as the scale-up
would have dramatically reduced the PLHIV number by 2030 (to ~612 thousand, compared to a
projected ~770 thousand if ART coverage was kept at 2015 levels).
Using available ART unit costs from the HIV Investment Case (R 3 879.26 per person per annum), this implies an investment of ~R 2 billion in year 2020 alone (assuming the same unit costs apply). This is about 50% more than current total expenditure including all HIV funding streams from domestic and external sources.
However, the growing funding necessary for expanding the ART programme in Johannesburg Health District may not always be available as required for the 2020 targets.
The Johannesburg 2016/17 HIV budget is only 5.8% higher (R 959 M) than the 2014/15 HIV expenditure (R 907 M). The CPI for urban areas annual inflation rate is 7,0%* and was 6,2% the previous year
This means the actual funding has decreased due to monetary inflation, aggravated by demographic growth
A funding freeze on the 2014/15 HIV budget would lead to:
decrease in actual ART coverage of PLHIV as the number of PLHIV continuously increases in this district
new infections and HIV deaths would rise very steeply in this context of ~3% demographic growth
ENDING AIDS IN JOHANNESBURG OCTOBER 2016
5
Epidemic projections for combined scale-up, Johannesburg (2010–30)
4. A realistic scale-up of other proven HIV interventions would yield a 5–10% reduction in total treatment costs whilst still achieving the three 90s.
Treatment needs (and
costs) are reduced if the
HCT/ART scale-up is
combined with medical
male circumcision, an
expanded condom
programme, and
comprehensive service
packages for FSWs and
young females (DREAMS
package). With this
combination approach, an
additional ~13 thousand
infections would be
averted 2016–30. 2030
targets would be reached
with ~526 thousand
people on ART, compared
to ~553 thousand if the
prevention packages were
not scaled-up. The
cumulative difference in
the annual treatment need
to 2030 would be ~285
thousand PLHIV (or R 1.1
billion saved at current
prices).
WHAT ARE THE CONCLUSIONS FROM THIS ANALYSIS?
a) A very large effort is needed: Analysis shows that the HCT/ART scale-up was rapid in the
last 5 years, but that a doubling of scale-up is needed to reach 2020 targets
b) Strategic investments in proven interventions such as medical male circumcision, an
expanded condom programme, and comprehensive packages for FSWs and young females
will help “get” Johannesburg the 90 targets (and with these, the 95 targets too)
c) Evidence-informed programmes for young women and adolescent girls (like DREAMS) are
likely to make a significant contribution to incidence reduction in these age groups, if
implemented at scale
d) An innovative mix of HIV testing approaches is needed to reach more PLHIV not sufficiently
covered with current services (an additional 100–160 thousand diagnoses needed by 2020,
and finding new HIV cases is becoming harder to achieve)
e) Rapid scale-up of funds is needed to achieve aspirational targets, especially in the context of
rising prices. Stagnant HIV budgets likely lead to increases in infections and deaths and
undermine the scale-up momentum the City of Johannesburg has gained
f) Analytical approaches supported by modelling can be useful to help set targets, monitor
progress and project the health and financial impacts
g) Johannesburg with its strong economic position and elevated human development offers
large opportunities for successful scale-up and as a city benefits from the proximity of
population to services, good communication networks, and a mix of providers.
ENDING AIDS IN JOHANNESBURG OCTOBER 2016
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COLLABORATORS
District of Johannesburg
Fredrika Mkhwanazi, Lihle Ngubane
Gauteng Province DOH
Emily Mabusela, Francis Akpan, Vusi Madi,
Khudugo Letsoalo, Eunice Sithole,
Nombuso Madonsela
National Department of Health
Yogan Pillay, Hasina Subedar, Peter
Barron, Batanayi Muzah
University of New South Wales/
Burnet Institute/Optima Consortium for
Decision Science
Robyn Stuart, Cliff Kerr, Andrew Shattock,
David Wilson
World Bank
Nicole Fraser, Paolo Belli, Thulani
Matsebula, Marelize Görgens*
* Corresponding author
CONTRIBUTORS
SANAC
Connie Kganakga
Stats SA
Chantal Munthree, Feroza Mohideen
SWEAT
Emay van Wijk, Dianne Massawe
OUT
Dawie Nel, Nelson Medeiros
ANOVA
Albert Ikhile, Vusi Sibeko, Nevashan
Govender, Charlotte Modibedi, Clairissa
Arendse
UCSF
Zachary Isdahl
HE2RO
Gesine Meyer-Rath, Lise Jamieson, Calvin
Chiu
Wits RHI
Vivian Morafo, Mohammed Majam
NICD
Tendesayi Kufa-Chakezha
CDC
Patrick Nadol
Strategic Development Consultant:
Steve Cohen
PEER REVIEW
Andrew Scheibe (Key populations specialist), Maxim Berdnikov (Portfolio Manager South Africa, Global Fund), and Peter Barron (NDOH Technical advisor)
1 Shisana O, Rehle, T., LC, S., Zuma, K., et al. South African National HIV Prevalence, Incidence and Behaviour
Survey, 2012. Cape Town: HSRC Press. 2014. 2 Estimate based on 2011 population census data and 2012 HIV survey data. 3 Buffalo City (formerly East London), City of Cape Town, City of Johannesburg, City of Tshwane, Ekurhuleni,
eThekwini, Mangaung (formerly Bloemfontein), and Nelson Mandela Bay 4 Statistics SA (http://www.statssa.gov.za/) 5 Stat SA, 2011 6 Department of Health of the Republic of South Africa (DoH) and South African National AIDS Council (NAC).
2016. South African HIV and TB Investment Case - Summary Report Phase 1. http://sanac.org.za/wp-content/uploads/2016/03/1603-Investment-Case-Report-LowRes-18-Mar.pdf. Accessed 17 June 2016.
7 www.optimamodel.com. Note that Optima HIV can also do geographic prioritisation, different service delivery modalities for HCT and ART, and long-term financial commitment analysis)
8 See http://optimamodel.com/publications.html for technical reports and journal articles, and http://optimamodel.com/results.html for a case study
9 Other model entries—HIV transmission probabilities: Based on international evidence (by type of sexual
intercourse, by injection practice, by breastfeeding status, by intervention), relative transmissibility during acute
infection phase 5.6x higher than after acute phase, increased HIV transmission at CD4<200 and at CD4<50,
reduction in transmissibility from virally suppressive ART=90% and from non-virally suppressive ART=50%.
HIV and mortality: Mortality for CD4<50: ~15% pa (if not on ART), TB co-factor 2.17. Treatment failure/drug
resistance: 5.4% p.a. VL suppression level of people reported on ART from secondary laboratory data analysis
of Johannesburg data: 54% of total patients reported on ART had a suppressed VL test result (69% of ART
patients had a test, and of these 78% had suppressed result). Age-related and risk-related population
transitions: based on demographic and behavioural data.